Publications

Order by: publication date number of citations

• Extending the planar theory of anyons to quantum wire networks

  Tomasz Maciazek, Mia Conlon, Gert Vercleyen, J. K. Slingerland

  SciPost Phys. 18, 074 (2025) · published 28 February 2025 | Toggle abstract · pdf

  The braiding of the worldlines of particles restricted to move on a network (graph) is governed by the graph braid group, which can be strikingly different from the standard braid group known from two-dimensional physics. It has been recently shown that imposing the compatibility of graph braiding with anyon fusion for anyons exchanging at a single wire junction leads to new types of anyon models with the braiding exchange operators stemming from solutions of certain generalised hexagon equations. In this work, we establish these graph-braided anyon fusion models for general wire networks. We show that the character of braiding strongly depends on the graph-theoretic connectivity of the given network. In particular, we prove that triconnected networks yield the same braiding exchange operators as the planar anyon models. In contrast, modular biconnected networks support independent braiding exchange operators in different modules. Consequently, such modular networks may lead to more efficient topological quantum computer circuits. Finally, we conjecture that the graph-braided anyon fusion models will possess the (generalised) coherence property where certain polygon equations determine the braiding exchange operators for an arbitrary number of anyons. We also extensively study solutions to these polygon equations for chosen low-rank multiplicity-free fusion rings, including the Ising theory, quantum double of $\mathbb{Z}_{2}$, and Tambara-Yamagami models. We find numerous solutions that do not appear in the planar theory of anyons.

• Investigating finite-size effects in random matrices by counting resonances

  Anton Kutlin, Carlo Vanoni

  SciPost Phys. 18, 090 (2025) · published 12 March 2025 | Toggle abstract · pdf

  Resonance counting is an intuitive and widely used tool in Random Matrix Theory and Anderson Localization. Its undoubted advantage is its simplicity: in principle, it is easily applicable to any random matrix ensemble. On the downside, the notion of resonance is ill-defined, and the "number of resonances" does not have a direct mapping to any commonly used physical observable like the participation entropy, the fractal dimensions, or the gap ratios (r-parameter), restricting the method's predictive power to the thermodynamic limit only where it can be used for locating the Anderson localization transition. In this work, we reevaluate the notion of resonances and relate it to measurable quantities, building a foundation for the future application of the method to finite-size systems. To access the HTML version of the paper & discuss it with the authors, visit https://enabla.com/pub/558.

• Immigration Detention at Sea: maritime migration governance in Australia and the sanctioning of indefinite detention for the purpose of removal

  Andonea Jon Dickson

  Mig. Pol. 3, 002 (2024) · published 19 June 2024 | Toggle abstract · pdf

  Over the past four decades, maritime geographies have become prominent sites of migration governance. While there is important scholarly work on these spaces, charting changing techniques of control and containment, what continues to demand attention is the governance that works through systematically keeping people seeking asylum mobile at sea. This article focuses on Australia, examining the coerced mobilities that follow interdictions at sea and their carceral nature. I interrogate the High Court case, CPCF v Minister for Immigration and Border Protection, which addresses the extended maritime detention of 157 people seeking asylum in June 2014. Through analysing the language used in this case, such as the conclusion by the majority that “to detain” a person at sea mandates a concomitant duty “to take” that person somewhere, I highlight how coerced mobility has become central to Australia’s strategy of maritime migration governance and how this has come to legitimate detention at sea without any clear time limitation. This will reveal the extent to which carcerality informs migration governance in Australia’s maritime geographies.

• Recent results on Central Exclusive Production with the STAR detector

  Rafal Sikora

  SciPost Phys. Proc. 8, 087 (2022) · published 13 July 2022 | Toggle abstract · pdf

  We present results on the Central Exclusive Production of charged particle pairs $h^{+}h^{-}$ ($h = \pi, K, p$), $pp\to p^\prime+h^{+}h^{-}+p^\prime$, obtained with the STAR experiment at RHIC in proton-proton collisions at a center-of-mass energy of $\sqrt{s} = 200$ GeV. All final-state particles were reconstructed, including forward-scattered protons detected in the Roman Pot system. As a result, the Double Pomeron Exchange (DPE) events were selected and the non-exclusive backgrounds were efficiently rejected. Differential fiducial cross sections were measured as functions of observables related to the central hadronic final state and to the forward-scattered protons. The measured cross sections were compared to phenomenological model predictions based on the DPE. We also present preliminary results on the measurement of the same physics process at higher collision energy $\sqrt{s} = 510$ GeV. The data demonstrate features similar to those observed at $\sqrt{s} = 200$ GeV.

• Longitudinal double-spin asymmetries of inclusive jet and dijet production at STAR

  Maria Zurek

  SciPost Phys. Proc. 8, 086 (2022) · published 12 July 2022 | Toggle abstract · pdf

  At the STAR experiment, the gluon helicity distribution $\Delta g(x, Q^2)$ can be accessed through the double-spin asymmetries $A_{LL}$ of inclusive jet and dijet production in collisions of longitudinally polarized protons. These proceedings report on recently published results of $A_{LL}$ for inclusive jets and dijets at $\sqrt{s} = 200$~GeV and $\sqrt{s} = 510$ GeV in mid-pseudorapidity ($|\eta| < 1$) and preliminary results for dijets in intermediate pseudrapidity ($-0.8 < \eta < 1.8$). The mid-pseudorapidity jet and dijet measurements are sensitive to $\Delta g(x, Q^2)$ for gluon momentum fractions $x$ from $\simeq 0.5$ down to $\simeq 0.015$; the sensitivity region for dijets in intermediate pseudorapidity at 510 GeV extends to even lower $x$. The measurements are in good agreement with previous STAR results and recent theoretical evaluations of prior world data.

• The quantum cartpole: A benchmark environment for non-linear reinforcement learning

  Kai Meinerz, Simon Trebst, Mark Rudner, Evert van Nieuwenburg

  SciPost Phys. Core 7, 026 (2024) · published 7 May 2024 | Toggle abstract · pdf

  Feedback-based control is the de-facto standard when it comes to controlling classical stochastic systems and processes. However, standard feedback-based control methods are challenged by quantum systems due to measurement induced backaction and partial observability. Here we remedy this by using weak quantum measurements and model-free reinforcement learning agents to perform quantum control. By comparing control algorithms with and without state estimators to stabilize a quantum particle in an unstable state near a local potential energy maximum, we show how a trade-off between state estimation and controllability arises. For the scenario where the classical analogue is highly nonlinear, the reinforcement learned controller has an advantage over the standard controller. Additionally, we demonstrate the feasibility of using transfer learning to develop a quantum control agent trained via reinforcement learning on a classical surrogate of the quantum control problem. Finally, we present results showing how the reinforcement learning control strategy differs from the classical controller in the non-linear scenarios.

• Dynamical spin-orbit-based spin transistor

  Fahriye N. Gürsoy, P. Reck, C. Gorini, K. Richter, I. Adagideli

  SciPost Phys. 14, 060 (2023) · published 4 April 2023 | Toggle abstract · pdf

  Spin-orbit interaction (SOI) has been a key tool to steer and manipulate spin-dependent transport properties in two-dimensional electron gases. Here we demonstrate how spin currents can be created and efficiently read out in nano- or mesoscale conductors with time-dependent and spatially inhomogeneous Rashba SOI. Invoking an underlying non-Abelian SU(2) gauge structure we show how time-periodic spin-orbit fields give rise to spin electric forces and enable the generation of pure spin currents of the order of several hundred nano-Amperes. In a complementary way, by combining gauge transformations with "hidden" Onsager relations, we exploit spatially inhomogeneous Rashba SOI to convert spin currents (back) into charge currents. In combining both concepts, we devise a spin transistor that integrates efficient spin current generation, by employing dynamical SOI, with its experimentally feasible detection via conversion into charge signals. We derive general expressions for the respective spin- and charge conductance, covering large parameter regimes of SOI strength and driving frequencies, far beyond usual adiabatic approaches such as the frozen scattering matrix approximation. We check our analytical expressions and approximations with full numerical spin-dependent transport simulations and demonstrate that the predictions hold true in a wide range from low to high driving frequencies.

• Rapid measurements and phase transition detections made simple by AC-GANs

  Jiewei Ding, Ho-Kin Tang, Wing Chi Yu

  SciPost Phys. Core 7, 035 (2024) · published 11 June 2024 | Toggle abstract · pdf

  In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited. Here, we investigate classical many-body systems using generative adversarial neural networks. We employ the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and integrate self-attention layers into the generator. This modification enables the network learn the distribution of the two-dimensional (2D) XY model's spin configurations and the physical quantities of interest. Utilizing the symmetry of the systems, we discover that AC-GAN can be trained with a very small raw dataset. This approach allows us to obtain reliable measurements for models typically demanding large samples, such as the large-sized 2D XY and the 3D constrained Heisenberg models. Moreover, we demonstrate the capability of AC-GAN to identify the phase transition points by quantifying the distribution changes in the spin configurations of the systems.

• CaloDREAM – Detector response emulation via attentive flow matching

  Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn

  SciPost Phys. 18, 088 (2025) · published 11 March 2025 | Toggle abstract · pdf

  Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. We show how combining Conditional Flow Matching with transformer elements allows us to simulate the detector phase space reliably. Namely, we use an autoregressive transformer to simulate the energy of each layer, and a vision transformer for the high-dimensional voxel distributions. We show how dimension reduction via latent diffusion allows us to train more efficiently and how diffusion networks can be evaluated faster with bespoke solvers. We showcase our framework, CaloDREAM, on datasets 2 and 3 of the CaloChallenge.

• Bayesian RG flow in neural network field theories

  Jessica N. Howard, Marc S. Klinger, Anindita Maiti, Alexander G. Stapleton

  SciPost Phys. Core 8, 027 (2025) · published 5 March 2025 | Toggle abstract · pdf

  The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme that generalizes the principles of the exact renormalization group (ERG) to arbitrarily parameterized probability distributions, including those of NNs. In BRG, coarse graining is performed in parameter space with respect to an information-theoretic distinguishability scale set by the Fisher information metric. In this paper, we unify NNFT and BRG to form a powerful new framework for exploring the space of NNs and SFTs, which we coin BRG-NNFT. With BRG-NNFT, NN training dynamics can be interpreted as inducing a flow in the space of SFTs from the information-theoretic 'IR' $→$ 'UV'. Conversely, applying an information-shell coarse graining to the trained network's parameters induces a flow in the space of SFTs from the information-theoretic 'UV' $→$ 'IR'. When the information-theoretic cutoff scale coincides with a standard momentum scale, BRG is equivalent to ERG. We demonstrate the BRG-NNFT correspondence on two analytically tractable examples. First, we construct BRG flows for trained, infinite-width NNs, of arbitrary depth, with generic activation functions. As a special case, we then restrict to architectures with a single infinitely-wide layer, scalar outputs, and generalized cos-net activations. In this case, we show that BRG coarse-graining corresponds exactly to the momentum-shell ERG flow of a free scalar SFT. Our analytic results are corroborated by a numerical experiment in which an ensemble of asymptotically wide NNs are trained and subsequently renormalized using an information-shell BRG scheme.